Fish-1

The main goal of this project is to predict toxicity of chemicals to fish. This goal might be achieved by combining toxicokinetic and toxicodynamic models to predict internal concentrations in fish tissues and to link from sub-organ scale to organism scale effects (see Figure 1.). If the link to effects on the level of organisms can be established this modelling approach can greatly reduce the need for animal testing to predict toxicity.

Figure 1. Linking from sub-organ to organism scale

1. Toxicokinetics

Toxicokinetic part describes processes of absorption, distribution, biotransformation and excretion of a toxicant (Krechniak 2005) and its goal is to describe the time course of the chemical concentration at the site of action (Kleinow, Nichols et al. 2008). Generally, two toxicant kinetics classes are distinguished: multi-compartment models (which describe the movement of chemicals between various compartments) and models based on one compartment assumption (Landrum, Lee et al. 1992; Kleinow, Nichols et al. 2008).

The main important issue in this project part is to choose a proper model which might be used for predicting internal concentration in fish. For this reason two one-compartment approaches and one physiologically based multi-compartment model will be compared to measured internal concentrations both in whole organism and in selected fish cells.

2. Toxicodynamics

Despite the huge role of toxicokinetic processes, this is only a first step in risk assessment, which does not only describe toxic properties qualitatively but also quantifies both exposure and toxic response (Heinrich-Hirsch, Madle et al. 2001). Thanks to toxicokinetic modelling it is possible to predict internal concentration of a chemical but for predicting the response of an organism for this toxicant, also knowledge about toxicodynamic processes is required. This approach includes all mechanisms through which the concentration at the action site causes a toxic effect on the organism (Heinrich-Hirsch, Madle et al. 2001). In addition, toxicodynamics is connected with mechanism of toxicant action and it bases on assumption that the concentration at the site of action is related to the range of effect (Heinrich-Hirsch, Madle et al. 2001). TD modelling may include for instance: binding to receptors, to DNA, changed gene expression, modifications in secretion of hormones, cytotoxicity, cell proliferation, chronic alterations in organ function, histopathology and physiology etc. (Conolly and Andersen 1991; Medinsky 1995; Heinrich-Hirsch, Madle et al. 2001).

The toxicodynamic part includes the biggest challenge of the whole project because predicting fish response based on internal concentrations of chemicals in selected cells requires knowledge about a huge number of processes which occur in fish organism. Toxicodynamic model will take into account also chronic exposure of toxicants so fish life cycle has to be known.

In this project part it is very important to find out how complex model should be, which metabolism processes in fish (if any) should be taken into account, which effect endpoint (like death, growth, energy budget etc.) are more sensitive than other and which equations should be used to describe this model properly. This model should be able to be adapted for other chemicals and fish species so required fish and toxicants’ parameters can not be very difficult to obtain. In addition, to develop and describe well this approach, some experiments will have to be carried out for model parameterisation. These experiments will be based on fish cells in vitro studies and thanks to them it will be possible to evaluate the model.

This project is directly connected with the Fish-2 project: “Extrapolating sub-lethal effects on fish to the population level”, which is carried out by Lara Ibrahim. Combining these two projects should deliver comprehensive information about impact of toxicant exposures on fish and fish populations.

Paper by Dr. Ben Martin “Predicting population dynamics from the properties of individuals: a cross-level test of dynamic energy budget theory” was chosen to receive an Honorable Mention for the 2013 Student Paper of the Year award from The American Naturalist (i.e., one of the three best papers among 80).

On March 10th, Dr. Chun Liu spoke at the Innovation Convention 2014, in the session “Nobel inspiration: a conversation with young researchers“